OpenRaiser/Intern-Atlas huggingface dataset:
Evaluation of Greedy and CBF for ETSI non-area GeoNetworking: The impact of DCC
Venue: arXiv cs.NI 2024
Year: 2024

This paper evaluates the performance of the two ETSI non-area forwarding algorithms in the GeoNetworking specification: Greedy Forwarding and Non-Area Contention-Based Forwarding (CBF). Non-area forwarding occurs when a packet is sent to a geographical Destination Area from a node located outside of this area, e.g., when a vehicle wants to alert of hazardous events to other vehicles located in a distant geographical area. The evaluation has been carried out both in urban and highway scenarios and takes into account the complete ETSI Architecture, including the interaction with the Decentralized Congestion Control (DCC) mechanism. We have also compared ETSI-defined mechanisms with optimizations found in the literature. Our main findings are that Greedy Forwarding, when combined with DCC, is extremely ineffective even with optimizations, and Non-Area CBFs (both ETSI CBF and an optimized version called S-FoT+) outperform Greedy Forwarding both in highway and urban scenarios.
Unlearning Backdoor Threats: Enhancing Backdoor Defense in Multimodal Contrastive Learning via Local Token Unlearning
Venue: arXiv cs.CV 2024
Year: 2024

Multimodal contrastive learning has emerged as a powerful paradigm for building high-quality features using the complementary strengths of various data modalities. However, the open nature of such systems inadvertently increases the possibility of backdoor attacks. These attacks subtly embed malicious behaviors within the model during training, which can be activated by specific triggers in the inference phase, posing significant security risks. Despite existing countermeasures through fine-tuning that reduce the adverse impacts of such attacks, these defenses often degrade the clean accuracy and necessitate the construction of extensive clean training pairs. In this paper, we explore the possibility of a less-cost defense from the perspective of model unlearning, that is, whether the model can be made to quickly \textbf{u}nlearn \textbf{b}ackdoor \textbf{t}hreats (UBT) by constructing a small set of poisoned samples. Specifically, we strengthen the backdoor shortcuts to discover suspicious samples through overfitting training prioritized by weak similarity samples. Building on the initial identification of suspicious samples, we introduce an innovative token-based localized forgetting training regime. This technique specifically targets the poisoned aspects of the model, applying a focused effort to unlearn the backdoor associations and trying not to damage the integrity of the overall model. Experimental results show that our method not only ensures a minimal success rate for attacks, but also preserves the model's high clean accuracy.
HT-LIP Model based Robust Control of Quadrupedal Robot Locomotion under Unknown Vertical Ground Motion
Venue: arXiv cs.RO 2024
Year: 2024

This paper presents a hierarchical control framework that enables robust quadrupedal locomotion on a dynamic rigid surface (DRS) with general and unknown vertical motions. The key novelty of the framework lies in its higher layer, which is a discrete-time, provably stabilizing footstep controller. The basis of the footstep controller is a new hybrid, time-varying, linear inverted pendulum (HT-LIP) model that is low-dimensional and accurately captures the essential robot dynamics during DRS locomotion. A new set of sufficient stability conditions are then derived to directly guide the controller design for ensuring the asymptotic stability of the HT-LIP model under general, unknown, vertical DRS motions. Further, the footstep controller is cast as a computationally efficient quadratic program that incorporates the proposed HT-LIP model and stability conditions. The middle layer takes the desired footstep locations generated by the higher layer as input to produce kinematically feasible full-body reference trajectories, which are then accurately tracked by a lower-layer torque controller. Hardware experiments on a Unitree Go1 quadrupedal robot confirm the robustness of the proposed framework under various unknown, aperiodic, vertical DRS motions and uncertainties (e.g., slippery and uneven surfaces, solid and liquid loads, and sudden pushes).
MRSch: Multi-Resource Scheduling for HPC
Venue: arXiv cs.DC 2024
Year: 2024

Emerging workloads in high-performance computing (HPC) are embracing significant changes, such as having diverse resource requirements instead of being CPU-centric. This advancement forces cluster schedulers to consider multiple schedulable resources during decision-making. Existing scheduling studies rely on heuristic or optimization methods, which are limited by an inability to adapt to new scenarios for ensuring long-term scheduling performance. We present an intelligent scheduling agent named MRSch for multi-resource scheduling in HPC that leverages direct future prediction (DFP), an advanced multi-objective reinforcement learning algorithm. While DFP demonstrated outstanding performance in a gaming competition, it has not been previously explored in the context of HPC scheduling. Several key techniques are developed in this study to tackle the challenges involved in multi-resource scheduling. These techniques enable MRSch to learn an appropriate scheduling policy automatically and dynamically adapt its policy in response to workload changes via dynamic resource prioritizing. We compare MRSch with existing scheduling methods through extensive tracebase simulations. Our results demonstrate that MRSch improves scheduling performance by up to 48% compared to the existing scheduling methods.
Impact of Video Compression Artifacts on Fisheye Camera Visual Perception Tasks
Venue: arXiv cs.CV 2024
Year: 2024

Autonomous driving systems require extensive data collection schemes to cover the diverse scenarios needed for building a robust and safe system. The data volumes are in the order of Exabytes and have to be stored for a long period of time (i.e., more than 10 years of the vehicle's life cycle). Lossless compression doesn't provide sufficient compression ratios, hence, lossy video compression has been explored. It is essential to prove that lossy video compression artifacts do not impact the performance of the perception algorithms. However, there is limited work in this area to provide a solid conclusion. In particular, there is no such work for fisheye cameras, which have high radial distortion and where compression may have higher artifacts. Fisheye cameras are commonly used in automotive systems for 3D object detection task. In this work, we provide the first analysis of the impact of standard video compression codecs on wide FOV fisheye camera images. We demonstrate that the achievable compression with negligible impact depends on the dataset and temporal prediction of the video codec. We propose a radial distortion-aware zonal metric to evaluate the performance of artifacts in fisheye images. In addition, we present a novel method for estimating affine mode parameters of the latest VVC codec, and suggest some areas for improvement in video codecs for the application to fisheye imagery.
ChatGPT Incorrectness Detection in Software Reviews
Venue: arXiv cs.SE 2024
Year: 2024

We conducted a survey of 135 software engineering (SE) practitioners to understand how they use Generative AI-based chatbots like ChatGPT for SE tasks. We find that they want to use ChatGPT for SE tasks like software library selection but often worry about the truthfulness of ChatGPT responses. We developed a suite of techniques and a tool called CID (ChatGPT Incorrectness Detector) to automatically test and detect the incorrectness in ChatGPT responses. CID is based on the iterative prompting to ChatGPT by asking it contextually similar but textually divergent questions (using an approach that utilizes metamorphic relationships in texts). The underlying principle in CID is that for a given question, a response that is different from other responses (across multiple incarnations of the question) is likely an incorrect response. In a benchmark study of library selection, we show that CID can detect incorrect responses from ChatGPT with an F1-score of 0.74 - 0.75.
Accuracy-Aware Cooperative Sensing and Computing for Connected Autonomous Vehicles
Venue: arXiv cs.NI 2024
Year: 2024

To maintain high perception performance among connected and autonomous vehicles (CAVs), in this paper, we propose an accuracy-aware and resource-efficient raw-level cooperative sensing and computing scheme among CAVs and road-side infrastructure. The scheme enables fined-grained partial raw sensing data selection, transmission, fusion, and processing in per-object granularity, by exploiting the parallelism among object classification subtasks associated with each object. A supervised learning model is trained to capture the relationship between the object classification accuracy and the data quality of selected object sensing data, facilitating accuracy-aware sensing data selection. We formulate an optimization problem for joint sensing data selection, subtask placement and resource allocation among multiple object classification subtasks, to minimize the total resource cost while satisfying the delay and accuracy requirements. A genetic algorithm based iterative solution is proposed for the optimization problem. Simulation results demonstrate the accuracy awareness and resource efficiency achieved by the proposed cooperative sensing and computing scheme, in comparison with benchmark solutions.
Leveraging Large Language Model to Generate a Novel Metaheuristic Algorithm with CRISPE Framework
Venue: arXiv cs.NE 2024
Year: 2024

In this paper, we borrow the large language model (LLM) ChatGPT-3.5 to automatically and quickly design a new metaheuristic algorithm (MA) with only a small amount of input. The novel animal-inspired MA named zoological search optimization (ZSO) draws inspiration from the collective behaviors of animals for solving continuous optimization problems. Specifically, the basic ZSO algorithm involves two search operators: the prey-predator interaction operator and the social flocking operator to balance exploration and exploitation well. Besides, the standard prompt engineering framework CRISPE (i.e., Capacity and Role, Insight, Statement, Personality, and Experiment) is responsible for the specific prompt design. Furthermore, we designed four variants of the ZSO algorithm with slight human-interacted adjustment. In numerical experiments, we comprehensively investigate the performance of ZSO-derived algorithms on CEC2014 benchmark functions, CEC2022 benchmark functions, and six engineering optimization problems. 20 popular and state-of-the-art MAs are employed as competitors. The experimental results and statistical analysis confirm the efficiency and effectiveness of ZSO-derived algorithms. At the end of this paper, we explore the prospects for the development of the metaheuristics community under the LLM era.
Re2LLM: Reflective Reinforcement Large Language Model for Session-based Recommendation
Venue: arXiv cs.AI 2024
Year: 2024

Large Language Models (LLMs) are emerging as promising approaches to enhance session-based recommendation (SBR), where both prompt-based and fine-tuning-based methods have been widely investigated to align LLMs with SBR. However, the former methods struggle with optimal prompts to elicit the correct reasoning of LLMs due to the lack of task-specific feedback, leading to unsatisfactory recommendations. Although the latter methods attempt to fine-tune LLMs with domain-specific knowledge, they face limitations such as high computational costs and reliance on open-source backbones. To address such issues, we propose a Reflective Reinforcement Large Language Model (Re2LLM) for SBR, guiding LLMs to focus on specialized knowledge essential for more accurate recommendations effectively and efficiently. In particular, we first design the Reflective Exploration Module to effectively extract knowledge that is readily understandable and digestible by LLMs. To be specific, we direct LLMs to examine recommendation errors through self-reflection and construct a knowledge base (KB) comprising hints capable of rectifying these errors. To efficiently elicit the correct reasoning of LLMs, we further devise the Reinforcement Utilization Module to train a lightweight retrieval agent. It learns to select hints from the constructed KB based on the task-specific feedback, where the hints can serve as guidance to help correct LLMs reasoning for better recommendations. Extensive experiments on multiple real-world datasets demonstrate that our method consistently outperforms state-of-the-art methods.
AeroBridge: Autonomous Drone Handoff System for Emergency Battery Service
Venue: arXiv cs.RO 2024
Year: 2024

This paper proposes an Emergency Battery Service (EBS) for drones in which an EBS drone flies to a drone in the field with a depleted battery and transfers a fresh battery to the exhausted drone. The authors present a unique battery transfer mechanism and drone localization that uses the Cross Marker Position (CMP) method. The main challenges include a stable and balanced transfer that precisely localizes the receiver drone. The proposed EBS drone mitigates the effects of downwash due to the vertical proximity between the drones by implementing diagonal alignment with the receiver, reducing the distance to 0.5 m between the two drones. CFD analysis shows that diagonal instead of perpendicular alignment minimizes turbulence, and the authors verify the actual system for change in output airflow and thrust measurements. The CMP marker-based localization method enables position lock for the EBS drone with up to 0.9 cm accuracy. The performance of the transfer mechanism is validated experimentally by successful mid-air transfer in 5 seconds, where the EBS drone is within 0.5 m vertical distance from the receiver drone, wherein 4m/s turbulence does not affect the transfer process.
InstUPR : Instruction-based Unsupervised Passage Reranking with Large Language Models
Venue: arXiv cs.CL 2024
Year: 2024

This paper introduces InstUPR, an unsupervised passage reranking method based on large language models (LLMs). Different from existing approaches that rely on extensive training with query-document pairs or retrieval-specific instructions, our method leverages the instruction-following capabilities of instruction-tuned LLMs for passage reranking without any additional fine-tuning. To achieve this, we introduce a soft score aggregation technique and employ pairwise reranking for unsupervised passage reranking. Experiments on the BEIR benchmark demonstrate that InstUPR outperforms unsupervised baselines as well as an instruction-tuned reranker, highlighting its effectiveness and superiority. Source code to reproduce all experiments is open-sourced at https://github.com/MiuLab/InstUPR
DeepMachining: Online Prediction of Machining Errors of Lathe Machines
Venue: arXiv cs.LG 2024
Year: 2024

We describe DeepMachining, a deep learning-based AI system for online prediction of machining errors of lathe machine operations. We have built and evaluated DeepMachining based on manufacturing data from factories. Specifically, we first pretrain a deep learning model for a given lathe machine's operations to learn the salient features of machining states. Then, we fine-tune the pretrained model to adapt to specific machining tasks. We demonstrate that DeepMachining achieves high prediction accuracy for multiple tasks that involve different workpieces and cutting tools. To the best of our knowledge, this work is one of the first factory experiments using pre-trained deep-learning models to predict machining errors of lathe machines.
Learning from Reduced Labels for Long-Tailed Data
Venue: arXiv cs.LG 2024
Year: 2024

Long-tailed data is prevalent in real-world classification tasks and heavily relies on supervised information, which makes the annotation process exceptionally labor-intensive and time-consuming. Unfortunately, despite being a common approach to mitigate labeling costs, existing weakly supervised learning methods struggle to adequately preserve supervised information for tail samples, resulting in a decline in accuracy for the tail classes. To alleviate this problem, we introduce a novel weakly supervised labeling setting called Reduced Label. The proposed labeling setting not only avoids the decline of supervised information for the tail samples, but also decreases the labeling costs associated with long-tailed data. Additionally, we propose an straightforward and highly efficient unbiased framework with strong theoretical guarantees to learn from these Reduced Labels. Extensive experiments conducted on benchmark datasets including ImageNet validate the effectiveness of our approach, surpassing the performance of state-of-the-art weakly supervised methods.
Model-less Is the Best Model: Generating Pure Code Implementations to Replace On-Device DL Models
Venue: arXiv cs.SE 2024
Year: 2024

Recent studies show that deployed deep learning (DL) models such as those of Tensor Flow Lite (TFLite) can be easily extracted from real-world applications and devices by attackers to generate many kinds of attacks like adversarial attacks. Although securing deployed on-device DL models has gained increasing attention, no existing methods can fully prevent the aforementioned threats. Traditional software protection techniques have been widely explored, if on-device models can be implemented using pure code, such as C++, it will open the possibility of reusing existing software protection techniques. However, due to the complexity of DL models, there is no automatic method that can translate the DL models to pure code. To fill this gap, we propose a novel method, CustomDLCoder, to automatically extract the on-device model information and synthesize a customized executable program for a wide range of DL models. CustomDLCoder first parses the DL model, extracts its backend computing units, configures the computing units to a graph, and then generates customized code to implement and deploy the ML solution without explicit model representation. The synthesized program hides model information for DL deployment environments since it does not need to retain explicit model representation, preventing many attacks on the DL model. In addition, it improves ML performance because the customized code removes model parsing and preprocessing steps and only retains the data computing process. Our experimental results show that CustomDLCoder improves model security by disabling on-device model sniffing. Compared with the original on-device platform (i.e., TFLite), our method can accelerate model inference by 21.8% and 24.3% on x86-64 and ARM64 platforms, respectively. Most importantly, it can significantly reduce memory consumption by 68.8% and 36.0% on x86-64 and ARM64 platforms, respectively.
PathoTune: Adapting Visual Foundation Model to Pathological Specialists
Venue: arXiv cs.CV 2024
Year: 2024

As natural image understanding moves towards the pretrain-finetune era, research in pathology imaging is concurrently evolving. Despite the predominant focus on pretraining pathological foundation models, how to adapt foundation models to downstream tasks is little explored. For downstream adaptation, we propose the existence of two domain gaps, i.e., the Foundation-Task Gap and the Task-Instance Gap. To mitigate these gaps, we introduce PathoTune, a framework designed to efficiently adapt pathological or even visual foundation models to pathology-specific tasks via multi-modal prompt tuning. The proposed framework leverages Task-specific Visual Prompts and Task-specific Textual Prompts to identify task-relevant features, along with Instance-specific Visual Prompts for encoding single pathological image features. Results across multiple datasets at both patch-level and WSI-level demonstrate its superior performance over single-modality prompt tuning approaches. Significantly, PathoTune facilitates the direct adaptation of natural visual foundation models to pathological tasks, drastically outperforming pathological foundation models with simple linear probing. The code is available at https://github.com/openmedlab/PathoDuet.
Medical Image Registration and Its Application in Retinal Images: A Review
Venue: arXiv cs.CV 2024
Year: 2024

Medical image registration is vital for disease diagnosis and treatment with its ability to merge diverse information of images, which may be captured under different times, angles, or modalities. Although several surveys have reviewed the development of medical image registration, these surveys have not systematically summarized methodologies of existing medical image registration methods. To this end, we provide a comprehensive review of these methods from traditional and deep learning-based directions, aiming to help audiences understand the development of medical image registration quickly. In particular, we review recent advances in retinal image registration at the end of each section, which has not attracted much attention. Additionally, we also discuss the current challenges of retinal image registration and provide insights and prospects for future research.
LARA: Linguistic-Adaptive Retrieval-Augmentation for Multi-Turn Intent Classification
Venue: arXiv cs.CL 2024
Year: 2024

Multi-turn intent classification is notably challenging due to the complexity and evolving nature of conversational contexts. This paper introduces LARA, a Linguistic-Adaptive Retrieval-Augmentation framework to enhance accuracy in multi-turn classification tasks across six languages, accommodating a large number of intents in chatbot interactions. LARA combines a fine-tuned smaller model with a retrieval-augmented mechanism, integrated within the architecture of LLMs. The integration allows LARA to dynamically utilize past dialogues and relevant intents, thereby improving the understanding of the context. Furthermore, our adaptive retrieval techniques bolster the cross-lingual capabilities of LLMs without extensive retraining and fine-tuning. Comprehensive experiments demonstrate that LARA achieves state-of-the-art performance on multi-turn intent classification tasks, enhancing the average accuracy by 3.67\% from state-of-the-art single-turn intent classifiers.
Human Understanding AI Paper Challenge 2024 -- Dataset Design
Venue: arXiv cs.LG 2024
Year: 2024

In 2024, we will hold a research paper competition (the third Human Understanding AI Paper Challenge) for the research and development of artificial intelligence technologies to understand human daily life. This document introduces the datasets that will be provided to participants in the competition, and summarizes the issues to consider in data processing and learning model development.
Harnessing the power of LLMs for normative reasoning in MASs
Venue: arXiv cs.AI 2024
Year: 2024

Software agents, both human and computational, do not exist in isolation and often need to collaborate or coordinate with others to achieve their goals. In human society, social mechanisms such as norms ensure efficient functioning, and these techniques have been adopted by researchers in multi-agent systems (MAS) to create socially aware agents. However, traditional techniques have limitations, such as operating in limited environments often using brittle symbolic reasoning. The advent of Large Language Models (LLMs) offers a promising solution, providing a rich and expressive vocabulary for norms and enabling norm-capable agents that can perform a range of tasks such as norm discovery, normative reasoning and decision-making. This paper examines the potential of LLM-based agents to acquire normative capabilities, drawing on recent Natural Language Processing (NLP) and LLM research. We present our vision for creating normative LLM agents. In particular, we discuss how the recently proposed "LLM agent" approaches can be extended to implement such normative LLM agents. We also highlight challenges in this emerging field. This paper thus aims to foster collaboration between MAS, NLP and LLM researchers in order to advance the field of normative agents.